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Where do we come from?

University of Udine The infoFACTORY Project An Initiative of the Artificial Intelligence Laboratory - Advanced Internet Applications prof. Carlo Tasso. Where do we come from?. Natural language processing Expert systems Intelligent information retrieval Intelligent user interfaces

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Where do we come from?

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  1. University of UdineThe infoFACTORY ProjectAn Initiative of the Artificial Intelligence Laboratory - Advanced Internet Applicationsprof. Carlo Tasso

  2. Where do we come from? • Natural language processing • Expert systems • Intelligent information retrieval • Intelligent user interfaces • User modelling

  3. Current Research Fields • Cognitive Web Information Filtering • Adaptive Web Personalization • Knowledge-Based Systems • Information Extraction and Sentiment Analysis

  4. Typical Application Areas · Content-based Information Filtering ·  News Delivery · Web Monitoring and Alerting · Automatic Classification&Categorization of Web Doc.s ·  Blogosphere monitoring · Mail Filtering and Alerting · Content-Based Web Crawling ·  Web Page Recommendation ·  Personalized Information Extraction ·  Digital Libraries

  5. intermediaries Information on the Web From Information Producers to Information Consumers ideas/concepts/events/ authors, information producers documents/multimedia docs./ /audio-video/ WEB/repositories/ data banks delivery (push) search (pull) unstructured info/ free text consumers

  6. Traditional Search Engines • Very fast • Very easy to use • Many results • Low cost BUT • Low precision • Not personalized

  7. … the consequence • Problems in searching, accessing, delivering… • Two perspectives: PULL & PUSH • Pull: • Oversupply, overload • Miss-retrieval (common accuracy of serach engines: 20%-30%) • Untimeliness • Push: • Miss-delivery • Oversupply • Waste • Untimeliness • WHY? Two difficult linguistic phenomena: Polisemy, Sinonimity are not considered

  8. The fundamental limitations of current approaches and tools • Key-word based: NO semantic analysis of texts • ‘One-size-fits all’ approach: all users are treated the same way, • NO comprehension of individual information needs and preferences • Presentation and delivery are the same for all users

  9. … inWeb 2.0 ….. ideas/concepts/events/opinions PRODUCER Documents/multimedia docs./ /audio-video/ WEB: sites, portals, blogs, social networks intermediaries delivery Search User Generated Content consumer Active user Active user Active user Active user Active user Active user Active user Active user Active user

  10. … inWeb 2.0 ….. ideas/concepts/events/opinions PRODUCER Documents/multimedia docs./ /audio-video/ WEB: sites, portals, blogs, social networks intermediaries delivery Search User Generated Content PROSUMERS

  11. DIGITAL TV (digital TV broadcast, satellite TV channels, interactive TV, streaming services, video-conferences, ...) Digital Content Space REPOSITORIES (data banks, DL 2.0, OPAC, ...) USER GENRATEED CONTENTS text/data, 2D/3D graphics, digital audio/video, … (any format, any standard) INTERNET (web sites, portals, news, directories, e-newpapers, e-mail, web TV/radio,web-cams, ...) Content Consumer Space infoFACTORY • - source discovery • - content identification • - content retrieval • - content filtering • content classification • information extraction • - content ranking • - content re-mixing • - c. recommendation • - content reformulation • - content monitoring • - content delivery • - content push • alerting • user profiling • conceptual analysis B2B - B2B2C (info producers, information brokers, info re-disributors, service&content providers, vertical portals, infomediators, web clippers,…) Personalized Information Services B2C (individual end users, virtual communities, associations, …) on PC, PDA, mobile devices (wap, gprs, umts,...), TV, playstation,...

  12. Automatic Personalization(in Information Access) Personalization means delivering: • the right content • to the right user • in the right moment and • in the most suitable way

  13. Adaptive Personalization • Adaptivity allows to automatically tailor system behaviour and processing to the specific characteristics of the (observed) user behaviour • Machine Learning, Conceptual Linguistic Analysis, Knowledge Representation, User Modeling, HCI, Intelligent Agents WEB 3.0

  14. Application Areas · Content-based Information Filtering ·  News Delivery · Web Monitoring and Alerting · Automatic Classification&Categorization of Web Doc.s ·  Blogosphere monitoring · Content-Based Web Crawling ·  Web Page Recommendation ·  Personalized Information Extraction ·  Digital Libraries

  15. CONTACT Prof. Carlo Tasso carlo.tasso@dimi.uniud.it

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